Article Excerpt: Lung cancer is the second most common type of cancer and leading cause of cancer death in men and women, with non-small cell lung cancer (NSCLC) accounting for up to 90% of cases. Somatic mutations heavily impact the sensitivity of NSCLC patients to various drug treatments, and are critical for choosing the most effective targeted therapies for this cancer. Most NSCLC patients develop resistance to their targeted therapies during the first year of treatment. The reason for resistance is still unknown. “Currently, there is no computational method to link information from medical records to somatic mutations and targeted therapy responses,” says Saeed Hassanpour, PhD, a computer scientist at Dartmouth’s Norris Cotton Cancer Center and an associate professor of Biomedical Data Science and Epidemiology at the Geisel School of Medicine. Hassanpour has received a 4-year $1.5M grant from the National Cancer Institute to build and validate machine learning approaches that can reveal relationships between clinical and pathologic findings, patient genetic profiles and drug resistance. Linking these data could mean better, personalized treatment strategies for NSCLC patients.
Full Article: https://tinyurl.com/y6o7zbjx
Article Source: Dartmouth Geisel School of Medicine News. Also posted in SC Online News.